% CVPR 2022 Paper Template
% based on the CVPR template provided by Ming-Ming Cheng (https://github.com/MCG-NKU/CVPR_Template)
% modified and extended by Stefan Roth (stefan.roth@NOSPAMtu-darmstadt.de)

\documentclass[10pt,twocolumn,letterpaper]{article}

%%%%%%%%% PAPER TYPE  - PLEASE UPDATE FOR FINAL VERSION
% \usepackage[review]{cvpr}      % To produce the REVIEW version
\usepackage{cvpr}              % To produce the CAMERA-READY version
%\usepackage[pagenumbers]{cvpr} % To force page numbers, e.g. for an arXiv version

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\usepackage[pagebackref,breaklinks,colorlinks]{hyperref}

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% It is strongly recommended to use hyperref, especially for the review version.
% hyperref with option pagebackref eases the reviewers' job.
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% ReviewTempalte.aux before re-running LaTeX.
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%  should be clear).
\usepackage[pagebackref,breaklinks,colorlinks]{hyperref}


% Support for easy cross-referencing
\usepackage[capitalize]{cleveref}
\crefname{section}{Sec.}{Secs.}
\Crefname{section}{Section}{Sections}
\Crefname{table}{Table}{Tables}
\crefname{table}{Tab.}{Tabs.}


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\newcommand{\EqRef}[1]{Eq. \ref{#1}}
\newcommand{\FigRef}[1]{Fig. \ref{#1}}

%%%%%%%%% PAPER ID  - PLEASE UPDATE
\def\cvprPaperID{5325} % *** Enter the CVPR Paper ID here
\def\confName{CVPR}
\def\confYear{2023}


\begin{document}

%%%%%%%%% TITLE - PLEASE UPDATE
\title{Integral Neural Networks}
\author{
Kirill Solodskikh\thanks{\hspace{0.3pt} The authors contributed equally to this work.} \thanks{\;Currently affiliated with \url{Garch-Lab}.}
Azim Kurbanov$^{\hspace{0.3pt} *\dag}$
Ruslan Aydarkhanov$^{\hspace{0.3pt} \dag}$ \\
Irina Zhelavskaya
Yury Parfenov
Dehua Song
Stamatios Lefkimmiatis\\
Huawei Noah’s Ark Lab
\\
{\tt \small \{kirillceo, azimcto, ruslancto\}@garch.me}\\
{\tt \small \{zhelavskaya.irina1, parfenov.yury, dehua.song, stamatios.lefkimmiatis\}@huawei.com}
}


% \twocolumn[{
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%     \maketitle
%     \begin{center}
%     \vspace{-1.2cm}
%         \includegraphics[width=1\linewidth]{imgs/overview15.png}
%         \captionof{figure}{\name An overview of the \textbf{\emph{LOGO}} dataset. LOGO is a multi-person long-form video dataset with frame-wise annotations on both action procedures (as shown in the second line) and formations (as shown in the third line, which reflects relations among actors) based on artistic swimming scenarios. It provides a potential for constructing an action quality assessment approach with the ability of modeling group information among actors. Longer video durations also challenge the ability of the method to aggregate long-term temporal information.}
%         \label{fig:datasetoverview}
%     \end{center}
% }]

% \makeatletter
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%     \begin{minipage}{\textwidth}
%         \vspace{-0.6cm}
%         \centering
%         \includegraphics[width=\linewidth]{imgs/overview15.png}
%         \captionof{figure}{An overview of the \textbf{\textit{LOGO}} dataset. LOGO is a multi-person long-form video dataset with frame-wise annotations on both action procedures (as shown in the second line) and formations (as shown in the third line, which reflects relations among actors) based on artistic swimming scenarios. It provides a potential for constructing an action quality assessment approach with the ability to model group information among actors. Longer video durations also challenge the ability of the method to aggregate long-term temporal information.}
%         \label{fig:datasetoverview}
%         \vspace{+0.6cm}
%     \end{minipage}}
% \makeatother

\maketitle
\input{sections/0_abstract}
\input{sections/1_introduction}
\input{sections/2_relatedwork}
\input{sections/3_neuralnetworks}

\input{sections/4_trainingnetwork}
\input{sections/5_experiments}
\input{sections/6_conclusion}

% \input{sections/7_demo}


%%%%%%%%% REFERENCES
{\small
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}

\end{document}
